Semantic segmentation of remote sensing images plays an important role in many applications. However, a remote sensing image typically comprises a complex and heterogenous urban landscape with ...objects in various sizes and materials, which causes challenges to the task. In this work, a novel adaptive fusion network (AFNet) is proposed to improve the performance of very high resolution (VHR) remote sensing image segmentation. To coherently label size-varied ground objects from different categories, we design multilevel architecture with the scale-feature attention module (SFAM). By SFAM, at the location of small objects, low-level features from the shallow layers of convolutional neural network (CNN) are enhanced, whilst for large objects, high-level features from deep layers are enhanced. Thus, the features of size-varied objects could be preserved during fusing features from different levels, which helps to label size-varied objects. As for labeling the category with high intra-class difference and varied scales, the multiscale structure with a scale-layer attention module (SLAM) is utilized to learn representative features, where an adjacent score map refinement module (ACSR) is employed as the classifier. By SLAM, when fusing multiscale features, based on the interested objects scale, feature map from appropriate scale is given greater weights. With such a scale-aware strategy, the learned features can be more representative, which is helpful to distinguish objects for semantic segmentation. Besides, the performance is further improved by introducing several nonlinear layers to the ACSR. Extensive experiments conducted on two well-known public high-resolution remote sensing image data sets show the effectiveness of our proposed model. Code and predictions are available at https://github.com/athauna/AFNet/
Objective:
Neuropathic pain (NP) associated with depression or anxiety is highly prevalent in clinical practice. Publications about NP associated with depression or anxiety increased exponentially ...from 2000 to 2020. However, studies that applied the bibliometric method in analyzing global scientific research about NP associated with depression or anxiety are rare. This work used the bibliometric method to analyze the publications on NP associated with depression or anxiety between 2000 and 2020.
Method:
Publications from 2000 and 2020 were identified from the Thomson Reuters Web of Science (WoS) database. We employed CiteSpace V to conduct the bibliometric study.
Results:
A total of 915 articles or reviews were obtained from the WoS database. The number of publications has increased over the last two decades. The USA was the most productive among countries or regions in the field. According to the burst key words, neuroinflammation, hippocampus, safety, and modulation were the hot global research issues in the domain.
Conclusion:
Publications about NP associated with depression or anxiety have remarkably increased from 2000 to 2020. These historical opinions about NP associated with depression or anxiety could be an important practical basis for further research into potential development trends.
▸ Ag/PVDF-g-PAA composite membrane is firstly prepared. ▸ Silver nanoparticle provides PVDF membrane a significant improvement of surface hydrophilicity. ▸ Silver nanoparticle endows PVDF membrane ...with outstanding antifouling (anti-organic fouling and anti-biofouling) performance.
In this study, silver nanoparticles were used to endow poly(vinylidene fluoride) (PVDF) membrane with excellent surface hydrophilicity and outstanding antifouling performance. Silver nanoparticles were successfully immobilized onto PVDF membrane surface under the presence of poly(acrylic acid) (PAA). The double effects of silver nanoparticles on PVDF membrane, i.e., surface hydrophilicity and anti-fouling performance, were systematically investigated. Judging from result of water static contact measurement, silver nanoparticles had provided a significant improvement in PVDF membrane surface hydrophilicity. And the possible explanation on the improvement of PVDF membrane surface hydrophilicity with silver nanoparticles was firstly proposed in this study. Membrane permeation and anti-bacterial tests were carried out to characterize the antifouling performance of PVDF membrane. Flux recovery ratio (FRR) increased about 40% after the presence of silver nanoparticles on the PVDF membrane surface, elucidating the anti-organic fouling performance of PVDF membrane was elevated by silver nanoparticles. Simultaneously, anti-bacterial test confirmed that PVDF membrane showed superior anti-biofouling activity because of silver nanoparticles. The above-mentioned results clarified that silver nanoparticles can endow PVDF membrane with both excellent surface hydrophilicity and outstanding antifouling performance in this study.
The CuCl‐catalyzed reaction of aryl boronic acid with carbon dioxide to form carboxylate ester after treatment with CH3I has been developed. The procedure featured mild conditions and good functional ...group tolerances. A diverse range of aryl boronic acids were effectively converted into carboxylate esters. Even those bearing sensitive groups such as carbonyl, ester, and amide could produce the desired products in good yields.
A readily available and mild CuCl catalyst system has been developed for converting CO2 with aryl boronic acids to aryl carboxylate esters. The system has a good functional group tolerance, and substrates containing electron‐withdrawing and electron‐donating groups could be converted into corresponding products with good yields.
An imidazolium bridged macrocyclophane was synthesized as a ratiometric fluorescence sensor with aggregation-induced emission (AIE) characteristic to detect pyrophosphate anion with high selectivity ...among various anions. In the presence of zinc ion, macrocyclophane can form aggregates through complexation with pyrophosphate anion and emit ratiometric fluorescence, resulting from an enhancement in its aggregate-state emission and a reduction in its monomer emission. This AIE-active macrocycle showed great potential as a ratiometric fluorescence receptor.
The fluorescence of an imidazolium macrocycle can be switched from monomer emission to aggregate emission in the presence of pyrophosphate anion and zinc ion, which can be used as a ratiometric fluorescence sensor for pyrophosphate anion in water.
Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals.
This paper explores the influence of the emotion recognition accuracy of EEG signals ...in different frequency bands and different number of channels.
We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier.
The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands.
This paper provided better frequency bands and channels reference for emotion recognition based on EEG.
The use of geophysical logging data to identify lithology is an important groundwork in logging interpretation. Inevitably, noise is mixed in during data collection due to the equipment and other ...external factors and this will affect the further lithological identification and other logging interpretation. Therefore, to get a more accurate lithological identification it is necessary to adopt de-noising methods. In this study, a new de-noising method, namely improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-wavelet transform, is proposed, which integrates the superiorities of improved CEEMDAN and wavelet transform. Improved CEEMDAN, an effective self-adaptive multi-scale analysis method, is used to decompose non-stationary signals as the logging data to obtain the intrinsic mode function (IMF) of N different scales and one residual. Moreover, one self-adaptive scale selection method is used to determine the reconstruction scale k. Simultaneously, given the possible frequency aliasing problem between adjacent IMFs, a wavelet transform threshold de-noising method is used to reduce the noise of the (k-1)th IMF. Subsequently, the de-noised logging data are reconstructed by the de-noised (k-1)th IMF and the remaining low-frequency IMFs and the residual. Finally, empirical mode decomposition, improved CEEMDAN, wavelet transform and the proposed method are applied for analysis of the simulation and the actual data. Results show diverse performance of these de-noising methods with regard to accuracy for lithological identification. Compared with the other methods, the proposed method has the best self-adaptability and accuracy in lithological identification.
In order to investigate the effects of feedstock ratio and organic loading rate (OLR) on the anaerobic mesophilic co-digestion of rice straw (RS) and cow manure (CM), batch tests (2.5L) were carried ...out at volatile solid (VS) ratios of 0:1, 1:2, 1:1, 2:1, and 1:0 (RS/CM), and continuous bench experiments (40 L) were carried out at OLRs of 3.0, 3.6, 4.2, 4.8, 6.0, 8.0, and 12.0 kg VS/(m(3) d) with optimal VS ratio. The optimal VS ratio was found to be 1:1. Stable and efficient co-digestion with average specific biogas production of 383.5L/kg VS and volumetric biogas production rate of 2.30 m(3)/(m(3) d) was obtained at an OLR of 6 kg VS/(m(3) d). Anaerobic co-digestion was severely inhibited by the accumulation of volatile fatty acids instead of ammonia when the OLR was 12 kg VS/(m(3) d). Further, significant foaming was observed at OLR ⩾ 8 kg VS/(m(3) d).
Thermal hazards of reactive chemicals have been a major concern due to the unceasing occurrences of fire and explosion accidents in industry. Understanding thermal threats of these chemicals not only ...contributes to the process safety and sustainability in the research and development (R&D) level, but promotes the efficiency of loss prevention, firefighting and emergency responses. Numerous studies have been conducted towards the comprehensive assessment of thermal hazards of reactive chemicals. The chemicals and methods varied in these studies, and yet some topics were commonly concerned by researchers in relevant fields. Further to say, these topics should also be targeted at when dealing with thermal hazards of new chemicals in future. This article provides an up-to-date overview of the common ground regarding the comprehensive understanding of thermal hazards of reactive chemicals in industry level; whereby, the main limitations and challenges to be faced are explored. The discussed key points include, the classification and reactivity of typical reactive chemicals, the fundamental steps towards the comprehensive understanding of their thermal hazards (including the identification of reaction mechanisms, the calculation of reaction kinetics and thermodynamics, and the characterization of thermal safety properties), and the applicable theoretical, experimental and engineering approaches in each step. The primary goal of this review is to lay out the essential basics that should be focused in every trial to comprehensively understand the thermal hazards of reactive chemicals. The further research directions are also presented based on the current research gaps and the context of Industry 4.0.
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With rapidly increased interests in biomass, diverse chemical and biological processes have been applied for biomass utilization. Fourier transform infrared (FTIR) analysis has been used for ...characterizing different types of biomass and their products, including natural and processed biomass. During biomass treatments, some solvents and/or catalysts can be retained and contaminate biomass. In addition, contaminants can be generated by the decomposition of biomass components. Herein, we report FTIR analyses of a series of contaminants, such as various solvents, chemicals, enzymes, and possibly formed degradation by-products in the biomass conversion process along with poplar biomass. This information helps to prevent misunderstanding the FTIR analysis results of the processed biomass.
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